{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5DEPSX4LR2XUAR3XOVRACLK6RP","short_pith_number":"pith:5DEPSX4L","schema_version":"1.0","canonical_sha256":"e8c8f95f8b8eaf4047777562012d5e8be4bfc839583b37069801488a5f20b0f1","source":{"kind":"arxiv","id":"2301.06412","version":1},"attestation_state":"computed","paper":{"title":"Enforcing Privacy in Distributed Learning with Performance Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.MA"],"primary_cat":"cs.LG","authors_text":"Ali H. Sayed, Elsa Rizk, Stefan Vlaski","submitted_at":"2023-01-16T13:03:27Z","abstract_excerpt":"We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is not well-tuned to the graph structure. For this reason, we exploit two alternative graph-homomorphic constructions and show that they improve performance while guaranteeing privacy. Moreover, contrary to most earlier studies, the gradient of the risks is not assumed to be bounded (a condition that rarely holds in practice; e.g., quadratic ris"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2301.06412","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-16T13:03:27Z","cross_cats_sorted":["cs.CR","cs.MA"],"title_canon_sha256":"6165615528cfc7608bb0804d18ccdbb50201a035246bd033b373af6167eeccdb","abstract_canon_sha256":"264d63a7d6c1a6f960f6897da4366732331258a504244dad3b0638003a9030c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:33:31.333882Z","signature_b64":"HXM54JAz7tNIQL1LSkiA6qTMWCFAnBuCpbAF8hsa57gdgpBfi1Fxt96kMm6M6LZqmpqU0MB9O8YUMiTUGK+UBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8c8f95f8b8eaf4047777562012d5e8be4bfc839583b37069801488a5f20b0f1","last_reissued_at":"2026-07-05T05:33:31.333423Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:33:31.333423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enforcing Privacy in Distributed Learning with Performance Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.MA"],"primary_cat":"cs.LG","authors_text":"Ali H. Sayed, Elsa Rizk, Stefan Vlaski","submitted_at":"2023-01-16T13:03:27Z","abstract_excerpt":"We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is not well-tuned to the graph structure. For this reason, we exploit two alternative graph-homomorphic constructions and show that they improve performance while guaranteeing privacy. Moreover, contrary to most earlier studies, the gradient of the risks is not assumed to be bounded (a condition that rarely holds in practice; e.g., quadratic ris"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.06412","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2301.06412/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2301.06412","created_at":"2026-07-05T05:33:31.333484+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.06412v1","created_at":"2026-07-05T05:33:31.333484+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.06412","created_at":"2026-07-05T05:33:31.333484+00:00"},{"alias_kind":"pith_short_12","alias_value":"5DEPSX4LR2XU","created_at":"2026-07-05T05:33:31.333484+00:00"},{"alias_kind":"pith_short_16","alias_value":"5DEPSX4LR2XUAR3X","created_at":"2026-07-05T05:33:31.333484+00:00"},{"alias_kind":"pith_short_8","alias_value":"5DEPSX4L","created_at":"2026-07-05T05:33:31.333484+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP","json":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP.json","graph_json":"https://pith.science/api/pith-number/5DEPSX4LR2XUAR3XOVRACLK6RP/graph.json","events_json":"https://pith.science/api/pith-number/5DEPSX4LR2XUAR3XOVRACLK6RP/events.json","paper":"https://pith.science/paper/5DEPSX4L"},"agent_actions":{"view_html":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP","download_json":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP.json","view_paper":"https://pith.science/paper/5DEPSX4L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.06412&json=true","fetch_graph":"https://pith.science/api/pith-number/5DEPSX4LR2XUAR3XOVRACLK6RP/graph.json","fetch_events":"https://pith.science/api/pith-number/5DEPSX4LR2XUAR3XOVRACLK6RP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP/action/storage_attestation","attest_author":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP/action/author_attestation","sign_citation":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP/action/citation_signature","submit_replication":"https://pith.science/pith/5DEPSX4LR2XUAR3XOVRACLK6RP/action/replication_record"}},"created_at":"2026-07-05T05:33:31.333484+00:00","updated_at":"2026-07-05T05:33:31.333484+00:00"}